Machine learning time-local generators of open quantum dynamics

Mazza, Paolo P; Zietlow, Dominik; Carollo, Federico; Andergassen, Sabine; Martius, Georg; Lesanovsky, Igor

Abstract

In the study of closed many-body quantum systems one is often interested in the evolution of a subset of degrees of freedom. On many occasions it is possible to approach the problem by performing an appropriate decomposition into a bath and a system. In the simplest case the evolution of the reduced state of the system is governed by a quantum master equation with a time-independent, i.e. Markovian, generator. Such evolution is typically emerging under the assumption of a weak coupling between the system and an infinitely large bath. Here, we are interested in understanding to which extent a neural network function approximator can predict open quantum dynamics-described by time-local generators-from an underlying unitary dynamics. We investigate this question using a class of spin models, which is inspired by recent experimental setups. We find that indeed time-local generators can be learned. In certain situations they are even time-independent and allow to extrapolate the dynamics to unseen times. This might be useful for situations in which experiments or numerical simulations do not allow to capture long-time dynamics and for exploring thermalization occurring in closed quantum systems.

Citation

Mazza, P. P., Zietlow, D., Carollo, F., Andergassen, S., Martius, G., & Lesanovsky, I. (2021). Machine learning time-local generators of open quantum dynamics. Physical Review Research, 3(2), Article 023084. https://doi.org/10.1103/physrevresearch.3.023084

• Journal Article Type: Article
• Acceptance Date: Mar 18, 2021
• Online Publication Date: Apr 30, 2021
• Publication Date: 2021-04
• Deposit Date: Mar 18, 2021
• Publicly Available Date: Mar 19, 2021
• Journal: Physical Review Research
• Electronic ISSN: 2643-1564
• Publisher: American Physical Society
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 2
• Article Number: 023084
• DOI: https://doi.org/10.1103/physrevresearch.3.023084
• Public URL: https://nottingham-repository.worktribe.com/output/5401757
• Publisher URL: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.023084